Image enhancement using the averaging histogram equalization (AVHEQ) approach for contrast improvement and brightness preservation

Lin, S.C.F.; Wong, Chin Yeow; Rahman, Arifur; Jiang, Guannan; Liu, S.; Kwok, Ngaiming; Shi, Hongxing; Yu, Ying-Hao; Wu, Tonghai

doi:10.1016/j.compeleceng.2015.06.001

Cited by 77 publications

(29 citation statements)

References 19 publications

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“…Even though histogram equalization has always been the most popular choice due to its implementation simplicity and satisfactory performance [11], histogram specification is adopted in this research due to its better capability in brightness preservation.…”

Section: B Histogram Specificationmentioning

confidence: 99%

Image de-hazing based on optimal compression and histogram specification

Liu

Rahman

Wong

et al. 2015

2015 8th International Congress on Image and Signal Processing (CISP)

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Haze in the environment will hinder the accurate recognition of objects captured in an image. To overcome this problem, image de-hazing processes have been an active technique applied in many research work. Among the available approaches, the one based on the assumption of dark channel prior is able to produce promising results and improved processing speed by integrating the guided filter. However, there are still some limitations existing in this method; particularly the over-range problem makes the appearance of recovered image unnatural. Moreover, its incapability in preserving image brightness frequently requires user intervention. In order to alleviate these shortcomings, the approach presented in this paper is realized through an effective magnitude compression operation. Histogram specification is further exercised for image postprocessing. Finally, parameters of both steps are optimized with the particle swarm optimization algorithm. Experiments were conducted with one hundred and thirty hazy images captured in different environmental conditions. Results showed that the proposed method performs better or equivalently in image dehazing comparing with the approach based on dark channel prior.

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Section: B Histogram Specificationmentioning

confidence: 99%

Image de-hazing based on optimal compression and histogram specification

Liu

Rahman

Wong

et al. 2015

2015 8th International Congress on Image and Signal Processing (CISP)

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“…Lin et al proposed a color image enhancement method called averaging histogram equalization (AVHEQ). 10 Images captured in poor lighting conditions can be degraded; this degradation can be restored using the AVHEQ method. The AVHEQ method fails to preserve the original mean brightness all over the image.…”

Section: Introductionmentioning

confidence: 99%

“…The DSBP method suffers from a drawback of preserving fine details in the enhanced portions of an output image. Lin et al proposed a color image enhancement method called averaging histogram equalization (AVHEQ) 10 . Images captured in poor lighting conditions can be degraded; this degradation can be restored using the AVHEQ method.…”

Section: Introductionmentioning

confidence: 99%

Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement

Subramani

Veluchamy

2020

Color Research & Application

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The detailed examination of low contrast image is a challenging issue. Thus, it makes difficult for the viewer to bring out the detailed features of the image. Histogram equalization (HE) is an efficient way to intensify the contrast of images. However, classical HE techniques result in immoderate intensification. Hence, an efficient contrast enhancement algorithm called quadrant dynamic clipped HE with gamma correction is proposed. This transformation addresses both over‐enhancement and fine detail preservation, which ensures no false contouring. In the proposed method, histogram of the input image is partitioned into four sections using its mean value. Histogram clipping and gamma correction is used to control the color enhancement rate. Then, clipped subhistogram is equalized independently and then they combined together to form an enhanced image. The performance assessment of the proposed and other existing methods is evaluated in terms of entropy, contrast, colorfulness, and saturation. Test results demonstrates that the proposed method outperforms the other existing HE methods in terms of preserving entropy, colorfulness, saturation, and obtaining uniform degree enhancement.

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“…Finally, all subimages are merged into a single image on the global grayscale histogram. The improved algorithms include brightness-preserving bi-HE (BBHE) [13,14], recursive mean-separate HE (RMSHE) [15][16][17], dualistic subimage HE (DSIHE) [18,19], minimum mean brightness error bi-HE (MMBEBHE) [20,21], and weighting meanseparated sub-HE (WMSHE) [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…The result maintains the mean of the original image. The idea of preserving brightness is also adopted in the literature [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

Zeng

Yan

Wang³

2016

Mathematical Problems in Engineering

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Cone beam computed tomography (CBCT) is a new detection method for 3D nondestructive testing of printed circuit boards (PCBs). However, the obtained 3D image of PCBs exhibits low contrast because of several factors, such as the occurrence of metal artifacts and beam hardening, during the process of CBCT imaging. Histogram equalization (HE) algorithms cannot effectively extend the gray difference between a substrate and a metal in 3D CT images of PCBs, and the reinforcing effects are insignificant. To address this shortcoming, this study proposes an image enhancement algorithm based on gray and its distance double-weighting HE. Considering the characteristics of 3D CT images of PCBs, the proposed algorithm uses gray and its distance double-weighting strategy to change the form of the original image histogram distribution, suppresses the grayscale of a nonmetallic substrate, and expands the grayscale of wires and other metals. The proposed algorithm also enhances the gray difference between a substrate and a metal and highlights metallic materials. The proposed algorithm can enhance the gray value of wires and other metals in 3D CT images of PCBs. It applies enhancement strategies of changing gray and its distance double-weighting mechanism to adapt to this particular purpose. The flexibility and advantages of the proposed algorithm are confirmed by analyses and experimental results.

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Image enhancement using the averaging histogram equalization (AVHEQ) approach for contrast improvement and brightness preservation

Cited by 77 publications

References 19 publications

Image de-hazing based on optimal compression and histogram specification

Image de-hazing based on optimal compression and histogram specification

Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement

Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

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